Attaining Higher Quality for Density Based Algorithms Morteza Haghir Chehreghani, Hassan Abolhassani, Mostafa Haghir Chehreghani. rr 2007 : 329-338 [doi] Mining Maximal Embedded Unordered Tree Patterns Mostafa Haghir Chehreghani , Masoud Rahgozar , Caro Lucas , Morteza Haghir Chehreghani .

Online Learning for Energy Efﬁcient Navigation using Contextual Information Master’s thesis in Computer science and engineering YONCA YUNATCI

Chalmers University (chalmers .se) Emilio Jorge, Morteza Haghir Chehreghani, Devdatt Dubhashi. May 28 Oct 22, 2019 Chalmers University of Technology Morteza Haghir Chehreghani, division of Data Science and AI) and Electrical Engineering (Assoc. Prof. Sep 19, 2016 Morteza Haghir Chehreghani profile image Morteza Haghir Chalmers, R.C., Member, S., Almeroth, K.C.: On the topology of multicast trees.

Chalmers. There might be more projects where Morteza Haghir Chehreghani participates, but you have to be logged in as a Chalmers employee to see them. Morteza Haghir Chehreghani Project with industry: Discovering novel chemical reactions through applying machine learning on knowledge graphs (*) Read more about Project with industry: Discovering novel chemical reactions through applying machine learning on knowledge graphs (*) Morteza Haghir Chehreghani Docent på avdelningen för Data Science och AI, Institutionen för data- och informationsteknik. morteza.chehreghani@chalmers.se +46317726415 Hitta till mig Read more about Active Learning for Artificial Neural Networks (Zoom link: https://chalmers.zoom.us/j/69556986938?pwd=TjFHTGhlNlJmRkRoMzVtNDBIRlBMUT09 password Morteza Haghir Chehreghani's research while affiliated with Chalmers University of Technology and other places. Morteza Haghir Chehreghani's research.

He holds a Ph.D. in Computer Science (AI/Machine Learning group) from ETH Zurich (2014). After the Ph.D., he joined Xerox Research as a Staff Research Morteza Haghir Chehreghani.

Chalmers University of Technology - ‪‪Cited by 549‬‬ - ‪Artificial Intelligence‬ - ‪ Machine Learning‬ - ‪Data Science‬

There might be more projects where Morteza Haghir Chehreghani participates, but you have to be logged in as a Chalmers employee to see them. Morteza Haghir Chehreghani Project with industry: Discovering novel chemical reactions through applying machine learning on knowledge graphs (*) Read more about Project with industry: Discovering novel chemical reactions through applying machine learning on knowledge graphs (*) Morteza Haghir Chehreghani Docent på avdelningen för Data Science och AI, Institutionen för data- och informationsteknik. morteza.chehreghani@chalmers.se +46317726415 Hitta till mig Read more about Active Learning for Artificial Neural Networks (Zoom link: https://chalmers.zoom.us/j/69556986938?pwd=TjFHTGhlNlJmRkRoMzVtNDBIRlBMUT09 password Morteza Haghir Chehreghani's research while affiliated with Chalmers University of Technology and other places. Morteza Haghir Chehreghani's research.

Mostafa Haghir Chehreghani, Morteza Haghir Chehreghani, Caro Lucas, Masoud Rahgozar: OInduced: An Efficient Algorithm for Mining Induced Patterns From Rooted Ordered Trees. IEEE Trans. Syst. Man Cybern. Part A 41 (5): 1013-1025 (2011)

(MLJ) 109(9-10), pp. 1779- 1802, 2020. Mäkeläinen2, Filip Slottner Seholm1, and Morteza Haghir Chehreghani1. 1 Department of Computer Science and Engineering.

12/20/2018 ∙ by Morteza Haghir Chehreghani, et al.
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Morteza Haghir Chehreghani, Hassan Abolhassani and Mostafa Haghir Chehreghani, Attaining higher quality for density based data mining algorithms, International Conference on Web Reasoning and Rule Systems , Springer Lecture Notes in Computer Sciences, vol. 4524, pp. 329-338, 2007.
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Carl Johnell, Morteza Haghir Chehreghani Chalmers University of Technology cjohnell@gmail.com, morteza.chehreghani@chalmers.se Abstract We study Frank-Wolfe algorithms – standard, pairwise, and away-steps – for efﬁcient optimization of Dominant Set Clus-tering. We present a uniﬁed and computationally efﬁcient

IEEE Trans. Syst. Man Cybern. Part A 41 (5): 1013-1025 (2011) Carl Johnell, Morteza Haghir Chehreghani Chalmers University of Technology cjohnell@gmail.com, morteza.chehreghani@chalmers.se Abstract We study Frank-Wolfe algorithms – standard, pairwise, and away-steps – for efﬁcient optimization of Dominant Set Clus-tering.